Inverse Problems in Learning from Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F10%3A00349057" target="_blank" >RIV/67985807:_____/10:00349057 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Inverse Problems in Learning from Data
Original language description
It is shown that application of methods from theory of inverse problems to learning from data leads to simple proofs of characterization of minima of empirical and expected error functionals and their regularized versions. The reformulation of learning in terms of inverse problems also enables comparison of regularized and non regularized case showing that regularization achieves stability by merely modifying output weights of global minima. Methods of theory of inverse problems lead to choice of reproducing kernel Hilbert spaces as suitable ambient function spaces.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/OC10047" target="_blank" >OC10047: Analysis of Intelligent Computational Distributed Systems</a><br>
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ICNC 2010. Proceedings of the International Conference on Neural Computation
ISBN
978-989-8425-32-4
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
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Publisher name
SciTePress
Place of publication
Setúbal
Event location
Valencia
Event date
Aug 24, 2010
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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